1 1. Load Data and Initial Exploration

import numpy as np
import pandas as pd

# Load dataset
df = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')
df.head()
##    customerID  gender  SeniorCitizen  ... MonthlyCharges TotalCharges  Churn
## 0  7590-VHVEG  Female              0  ...          29.85        29.85     No
## 1  5575-GNVDE    Male              0  ...          56.95       1889.5     No
## 2  3668-QPYBK    Male              0  ...          53.85       108.15    Yes
## 3  7795-CFOCW    Male              0  ...          42.30      1840.75     No
## 4  9237-HQITU  Female              0  ...          70.70       151.65    Yes
## 
## [5 rows x 21 columns]
df.columns.tolist()
## ['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn']
df.isnull().sum()
## customerID          0
## gender              0
## SeniorCitizen       0
## Partner             0
## Dependents          0
## tenure              0
## PhoneService        0
## MultipleLines       0
## InternetService     0
## OnlineSecurity      0
## OnlineBackup        0
## DeviceProtection    0
## TechSupport         0
## StreamingTV         0
## StreamingMovies     0
## Contract            0
## PaperlessBilling    0
## PaymentMethod       0
## MonthlyCharges      0
## TotalCharges        0
## Churn               0
## dtype: int64
df.describe()
##        SeniorCitizen       tenure  MonthlyCharges
## count    7043.000000  7043.000000     7043.000000
## mean        0.162147    32.371149       64.761692
## std         0.368612    24.559481       30.090047
## min         0.000000     0.000000       18.250000
## 25%         0.000000     9.000000       35.500000
## 50%         0.000000    29.000000       70.350000
## 75%         0.000000    55.000000       89.850000
## max         1.000000    72.000000      118.750000
df.shape
## (7043, 21)
df.info()
## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 7043 entries, 0 to 7042
## Data columns (total 21 columns):
##  #   Column            Non-Null Count  Dtype  
## ---  ------            --------------  -----  
##  0   customerID        7043 non-null   object 
##  1   gender            7043 non-null   object 
##  2   SeniorCitizen     7043 non-null   int64  
##  3   Partner           7043 non-null   object 
##  4   Dependents        7043 non-null   object 
##  5   tenure            7043 non-null   int64  
##  6   PhoneService      7043 non-null   object 
##  7   MultipleLines     7043 non-null   object 
##  8   InternetService   7043 non-null   object 
##  9   OnlineSecurity    7043 non-null   object 
##  10  OnlineBackup      7043 non-null   object 
##  11  DeviceProtection  7043 non-null   object 
##  12  TechSupport       7043 non-null   object 
##  13  StreamingTV       7043 non-null   object 
##  14  StreamingMovies   7043 non-null   object 
##  15  Contract          7043 non-null   object 
##  16  PaperlessBilling  7043 non-null   object 
##  17  PaymentMethod     7043 non-null   object 
##  18  MonthlyCharges    7043 non-null   float64
##  19  TotalCharges      7043 non-null   object 
##  20  Churn             7043 non-null   object 
## dtypes: float64(1), int64(2), object(18)
## memory usage: 1.1+ MB

2 2. Explore Categorical Variables

print(df["gender"].unique())
## ['Female' 'Male']
print(df['SeniorCitizen'].unique())
## [0 1]
np.sum(df['Churn'] == 'Yes'), np.sum(df['Churn'] == 'No')
## (np.int64(1869), np.int64(5174))
(df['Churn'] == 'Yes').mean(), (df['Churn'] == 'No').mean()
## (np.float64(0.2653698707936959), np.float64(0.7346301292063041))

3 3. Display Unique Values for Non-Numerical Columns

numerical_list = ['tenure', 'MonthlyCharges', 'TotalCharges']

for col in df.columns:
    if col not in numerical_list:
        print(f"{col}: {df[col].unique()}")
        print()
## customerID: ['7590-VHVEG' '5575-GNVDE' '3668-QPYBK' ... '4801-JZAZL' '8361-LTMKD'
##  '3186-AJIEK']
## 
## gender: ['Female' 'Male']
## 
## SeniorCitizen: [0 1]
## 
## Partner: ['Yes' 'No']
## 
## Dependents: ['No' 'Yes']
## 
## PhoneService: ['No' 'Yes']
## 
## MultipleLines: ['No phone service' 'No' 'Yes']
## 
## InternetService: ['DSL' 'Fiber optic' 'No']
## 
## OnlineSecurity: ['No' 'Yes' 'No internet service']
## 
## OnlineBackup: ['Yes' 'No' 'No internet service']
## 
## DeviceProtection: ['No' 'Yes' 'No internet service']
## 
## TechSupport: ['No' 'Yes' 'No internet service']
## 
## StreamingTV: ['No' 'Yes' 'No internet service']
## 
## StreamingMovies: ['No' 'Yes' 'No internet service']
## 
## Contract: ['Month-to-month' 'One year' 'Two year']
## 
## PaperlessBilling: ['Yes' 'No']
## 
## PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'
##  'Credit card (automatic)']
## 
## Churn: ['No' 'Yes']

4 4. Clean and Convert TotalCharges

len(df[df['TotalCharges']== ' '])
## 11
df['TotalCharges'] = df['TotalCharges'].replace({" ": "0.0"})
df['TotalCharges'] = df['TotalCharges'].astype(float)
df.info()
## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 7043 entries, 0 to 7042
## Data columns (total 21 columns):
##  #   Column            Non-Null Count  Dtype  
## ---  ------            --------------  -----  
##  0   customerID        7043 non-null   object 
##  1   gender            7043 non-null   object 
##  2   SeniorCitizen     7043 non-null   int64  
##  3   Partner           7043 non-null   object 
##  4   Dependents        7043 non-null   object 
##  5   tenure            7043 non-null   int64  
##  6   PhoneService      7043 non-null   object 
##  7   MultipleLines     7043 non-null   object 
##  8   InternetService   7043 non-null   object 
##  9   OnlineSecurity    7043 non-null   object 
##  10  OnlineBackup      7043 non-null   object 
##  11  DeviceProtection  7043 non-null   object 
##  12  TechSupport       7043 non-null   object 
##  13  StreamingTV       7043 non-null   object 
##  14  StreamingMovies   7043 non-null   object 
##  15  Contract          7043 non-null   object 
##  16  PaperlessBilling  7043 non-null   object 
##  17  PaymentMethod     7043 non-null   object 
##  18  MonthlyCharges    7043 non-null   float64
##  19  TotalCharges      7043 non-null   float64
##  20  Churn             7043 non-null   object 
## dtypes: float64(2), int64(2), object(17)
## memory usage: 1.1+ MB

5 5. Churn Distribution (Count & Percentage)

# Count and percentage of Churn
churn_counts = df['Churn'].value_counts()
churn_percentages = df['Churn'].value_counts(normalize=True) * 100

# Combine into a DataFrame for nice display
churn_dist = pd.DataFrame({
    'Count': churn_counts,
    'Percentage': churn_percentages.round(1)
})

print("การกระจายของตัวแปรเป้าหมาย (Churn):")
## การกระจายของตัวแปรเป้าหมาย (Churn):
print(churn_dist)
##        Count  Percentage
## Churn                   
## No      5174        73.5
## Yes     1869        26.5

6 6. Contract Type Distribution

# Count and percentage of Contract
contract_counts = df['Contract'].value_counts()
contract_percentages = df['Contract'].value_counts(normalize=True) * 100

# Combine into a DataFrame
contract_dist = pd.DataFrame({
    'Count': contract_counts,
    'Percentage': contract_percentages.round(1)
})

print("\nการกระจายของประเภทสัญญา (Contract):")
## 
## การกระจายของประเภทสัญญา (Contract):
print(contract_dist)
##                 Count  Percentage
## Contract                         
## Month-to-month   3875        55.0
## Two year         1695        24.1
## One year         1473        20.9

7 7. Visualizations

7.1 Churn and Contract Distribution

import matplotlib.pyplot as plt
import seaborn as sns

fig, ax = plt.subplots(1, 2, figsize=(12, 5))

sns.countplot(data=df, x='Churn', ax=ax[0])
ax[0].set_title('Churn Distribution')

sns.countplot(data=df, x='Contract', ax=ax[1])
ax[1].set_title('Contract Type Distribution')

plt.tight_layout()
plt.show()  # 👈 Essential for display in R Markdown

7.2 Histogram of Monthly Charges

plt.figure(figsize=(10, 6))
sns.histplot(df['MonthlyCharges'], bins=30, kde=True, color='skyblue')
plt.title('Histogram of Monthly Charges (Bimodal)')
plt.xlabel('Monthly Charges ($)')
plt.ylabel('Number of Customers')
plt.grid(True, linestyle='--', alpha=0.7)
plt.show()  # 👈 Essential

7.3 Box Plot: Tenure vs Churn

plt.figure(figsize=(8, 6))
sns.boxplot(data=df, x='Churn', y='tenure', palette='Set2')
plt.title('Box Plot: Tenure vs Churn')
plt.xlabel('Churn (Cancel service?)')
plt.ylabel('Tenure (Months)')
plt.grid(True, linestyle='--', alpha=0.7)
plt.show()  # 👈 Essential

7.4 Average Tenure by Churn Status

avg_tenure = df.groupby('Churn')['tenure'].mean().round(1)
print("ค่าเฉลี่ยของ tenure แยกตาม Churn:")
## ค่าเฉลี่ยของ tenure แยกตาม Churn:
for churn_status, avg in avg_tenure.items():
    print(f"ลูกค้าที่{'ยกเลิก' if churn_status == 'Yes' else 'ไม่ยกเลิก'}บริการ: {avg} เดือน")
## ลูกค้าที่ไม่ยกเลิกบริการ: 37.6 เดือน
## ลูกค้าที่ยกเลิกบริการ: 18.0 เดือน

8 8. FacetGrid: Tenure vs Monthly Charges by Contract and Churn

# Ensure TotalCharges is numeric
df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')
df['TotalCharges'] = df['TotalCharges'].fillna(0)

# Create FacetGrid
g = sns.FacetGrid(df, col='Contract', hue='Churn', col_wrap=3, height=4, aspect=1.2, palette=['#1f77b4', '#d62728'])
g.map(sns.scatterplot, 'tenure', 'MonthlyCharges', alpha=0.7)

g.add_legend()

# Set titles
axes = g.axes.flatten()
titles = ['Month-to-month', 'One year', 'Two year']
for ax, title in zip(axes, titles):
    ax.set_title(title)

# DO NOT create new plt.figure() — it breaks layout
# plt.figure(figsize=(20, 10))  ❌ Remove this line

plt.suptitle('Churn Distribution by Tenure, Monthly Charges, and Contract', y=1.02, fontsize=16)
plt.tight_layout()
plt.show()  # 👈 Essential

9 9. Key Metrics Summary

9.1 Churn Rate

print("1. อัตราการยกเลิกบริการ (Churn Rate)")
## 1. อัตราการยกเลิกบริการ (Churn Rate)
churn_rate = df['Churn'].value_counts(normalize=True) * 100
print(churn_rate)
## Churn
## No     73.463013
## Yes    26.536987
## Name: proportion, dtype: float64

9.2 Contract Distribution

print("2. สัดส่วนลูกค้าตามประเภทสัญญา")
## 2. สัดส่วนลูกค้าตามประเภทสัญญา
contract_distribution = df['Contract'].value_counts(normalize=True) * 100
print(contract_distribution)
## Contract
## Month-to-month    55.019168
## Two year          24.066449
## One year          20.914383
## Name: proportion, dtype: float64

9.3 Average Tenure by Churn

print("3. ค่าเฉลี่ย tenure (ระยะเวลาใช้งาน) แยกตามสถานะ Churn")
## 3. ค่าเฉลี่ย tenure (ระยะเวลาใช้งาน) แยกตามสถานะ Churn
tenure_by_churn = df.groupby('Churn')['tenure'].mean()
print(tenure_by_churn)
## Churn
## No     37.569965
## Yes    17.979133
## Name: tenure, dtype: float64

10 10. Correlation Heatmap

plt.figure(figsize=(10,8))
sns.heatmap(df[['tenure','MonthlyCharges','TotalCharges']].corr(), annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Heatmap')
plt.show()  # 👈 Essential

11 11. Count Plots for Categorical Variables

object_cols = df.select_dtypes(include="object").columns.to_list()
object_cols = ['SeniorCitizen'] + object_cols  # SeniorCitizen is int but treated as categorical

for col in object_cols:
    plt.figure(figsize=(8,5))
    sns.countplot(x=df[col])
    plt.title(f'Count Plot of {col}')
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.show()  # 👈 Essential
## <Figure size 800x500 with 0 Axes>
## <Axes: xlabel='SeniorCitizen', ylabel='count'>
## Text(0.5, 1.0, 'Count Plot of SeniorCitizen')
## ([0, 1], [Text(0, 0, '0'), Text(1, 0, '1')])
## <Figure size 800x500 with 0 Axes>
## <Axes: xlabel='customerID', ylabel='count'>
## Text(0.5, 1.0, 'Count Plot of customerID')
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## <Figure size 800x500 with 0 Axes>
## <Axes: xlabel='gender', ylabel='count'>
## Text(0.5, 1.0, 'Count Plot of gender')
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## <Figure size 800x500 with 0 Axes>
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## Text(0.5, 1.0, 'Count Plot of Partner')
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## <Figure size 800x500 with 0 Axes>
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## <Figure size 800x500 with 0 Axes>
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## <Figure size 800x500 with 0 Axes>
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## <Figure size 800x500 with 0 Axes>
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